+
+"""Contains class Model; main class user interacts with."""
+
+import numpy as np
+import pandas as pd
+import textdistance as td
+import itertools as it
+import copy as cp
+import seaborn as sns
+import joblib as jl
+
+from opqua.internal.host import Host
+from opqua.internal.vector import Vector
+from opqua.internal.population import Population
+from opqua.internal.setup import Setup
+from opqua.internal.intervention import Intervention
+from opqua.internal.gillespie import Gillespie
+from opqua.internal.data import saveToDf, getPathogens, getProtections, \
+ getPathogenDistanceHistoryDf
+from opqua.internal.plot import populationsPlot, compartmentPlot, \
+ compositionPlot, clustermap
+
+
+
[docs]
+
class Model(object):
+
"""Class defines a Model.
+
+
This is the main class that the user interacts with.
+
+
The Model class contains populations, setups, and interventions to be used
+
in simulation. Also contains groups of hosts/vectors for manipulations and
+
stores model history as snapshots for each time point.
+
+
**CONSTANTS:**
+
+
- `CB_PALETTE`: a colorblind-friendly 8-color color scheme.
+
- `DEF_CMAP`: a colormap object for Seaborn plots.
+
+
Attributes:
+
populations: dictionary with keys=population IDs, values=Population
+
objects.
+
setups: dictionary with keys=setup IDs, values=Setup objects.
+
interventions: contains model interventions in the order they will occur.
+
groups: dictionary with keys=group IDs, values=lists of hosts/vectors.
+
history: dictionary with keys=time values, values=Model objects that
+
are snapshots of Model at that timepoint.
+
t_var: variable that tracks time in simulations.
+
"""
+
+
### CONSTANTS ###
+
### Color scheme constants ###
+
CB_PALETTE = ["#E69F00", "#56B4E9", "#009E73",
+
"#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#999999"]
+
# www.cookbook-r.com/Graphs/Colors_(ggplot2)/#a-colorblind-friendly-palette
+
# http://jfly.iam.u-tokyo.ac.jp/color/
+
+
DEF_CMAP = sns.cubehelix_palette(
+
start=.5, rot=-.75, as_cmap=True, reverse=True
+
)
+
+
+
### CLASS CONSTRUCTOR ###
+
+
def __init__(self):
+
"""Create a new Model object."""
+
super(Model, self).__init__()
+
self.populations = {}
+
# dictionary with keys=population IDs, values=Population objects
+
self.setups = {}
+
# dictionary with keys=setup IDs, values=Setup objects
+
self.interventions = []
+
# contains model interventions in the order they will occur
+
self.groups = {}
+
# dictionary with keys=group IDs, values=lists of hosts/vectors
+
self.history = {}
+
# dictionary with keys=time values, values=Model objects that are
+
# snapshots of Model at that timepoint
+
self.global_trackers = {
+
# dictionary keeping track of some global indicators over all
+
# the course of the simulation
+
'num_events' : { id:0 for id in Gillespie.EVENT_IDS.values() },
+
# tracks the number of each kind of event in the simulation
+
'last_event_time' : 0,
+
# time point at which the last event in the simulation happened
+
'genomes_seen' : [],
+
# list of all unique genomes that have appeared in the
+
# simulation
+
'custom_conditions' : {}
+
# dictionary with keys=ID of custom condition, values=lists of
+
# times; every time True is returned by a function in
+
# custom_condition_trackers, the simulation time will be stored
+
# under the corresponding ID inside
+
# global_trackers['custom_condition']
+
}
+
self.custom_condition_trackers = {}
+
# dictionary with keys=ID of custom condition, values=functions that
+
# take a Model object as argument and return True or False; every
+
# time True is returned by a function in custom_condition_trackers,
+
# the simulation time will be stored under the corresponding ID
+
# inside global_trackers['custom_condition']
+
+
self.t_var = 0 # used as time variable during simulation
+
+
### MODEL METHODS ###
+
+
### Model initialization and simulation: ###
+
+
+
[docs]
+
def setRandomSeed(self, seed):
+
"""Set random seed for numpy random number generator.
+
+
Arguments:
+
seed (int): int for the random seed to be passed to numpy.
+
"""
+
+
np.random.seed(seed)
+
+
+
+
[docs]
+
def newSetup(
+
self, name, preset=None,
+
num_loci=None, possible_alleles=None,
+
fitnessHost=None, contactHost=None, receiveContactHost=None,
+
mortalityHost=None, natalityHost=None,
+
recoveryHost=None, migrationHost=None,
+
populationContactHost=None, receivePopulationContactHost=None,
+
mutationHost=None,
+
recombinationHost=None, fitnessVector=None,
+
contactVector=None, receiveContactVector=None, mortalityVector=None,
+
natalityVector=None, recoveryVector=None,
+
migrationVector=None, populationContactVector=None,
+
receivePopulationContactVector=None,
+
mutationVector=None, recombinationVector=None,
+
contact_rate_host_vector=None,
+
transmission_efficiency_host_vector=None,
+
transmission_efficiency_vector_host=None,
+
contact_rate_host_host=None,
+
transmission_efficiency_host_host=None,
+
mean_inoculum_host=None, mean_inoculum_vector=None,
+
recovery_rate_host=None, recovery_rate_vector=None,
+
mortality_rate_host=None, mortality_rate_vector=None,
+
recombine_in_host=None, recombine_in_vector=None,
+
num_crossover_host=None, num_crossover_vector=None,
+
mutate_in_host=None, mutate_in_vector=None,
+
death_rate_host=None, death_rate_vector=None,
+
birth_rate_host=None, birth_rate_vector=None,
+
vertical_transmission_host=None, vertical_transmission_vector=None,
+
inherit_protection_host=None, inherit_protection_vector=None,
+
protection_upon_recovery_host=None,
+
protection_upon_recovery_vector=None):
+
"""Create a new `Setup`, save it in setups dict under given name.
+
+
Two preset setups exist: "vector-borne" and "host-host". You may select
+
one of the preset setups with the preset keyword argument and then
+
modify individual parameters with additional keyword arguments, without
+
having to specify all of them.
+
+
**"host-host":**
+
+
- `num_loci` = 10
+
- `possible_alleles` = 'ATCG'
+
- `fitnessHost` = (lambda g: 1)
+
- `contactHost` = (lambda g: 1)
+
- `receiveContactHost` = (lambda g: 1)
+
- `mortalityHost` = (lambda g: 1)
+
- `natalityHost` = (lambda g: 1)
+
- `recoveryHost` = (lambda g: 1)
+
- `migrationHost` = (lambda g: 1)
+
- `populationContactHost` = (lambda g: 1)
+
- `receivePopulationContactHost` = (lambda g: 1)
+
- `mutationHost` = (lambda g: 1)
+
- `recombinationHost` = (lambda g: 1)
+
- `fitnessVector` = (lambda g: 1)
+
- `contactVector` = (lambda g: 1)
+
- `receiveContactVector` = (lambda g: 1)
+
- `mortalityVector` = (lambda g: 1)
+
- `natalityVector` = (lambda g: 1)
+
- `recoveryVector` = (lambda g: 1)
+
- `migrationVector` = (lambda g: 1)
+
- `populationContactVector` = (lambda g: 1)
+
- `receivePopulationContactVector` = (lambda g: 1)
+
- `mutationVector` = (lambda g: 1)
+
- `recombinationVector` = (lambda g: 1)
+
- `contact_rate_host_vector` = 0
+
- `transmission_efficiency_host_vector` = 0
+
- `transmission_efficiency_vector_host` = 0
+
- `contact_rate_host_host` = 2e-1
+
- `transmission_efficiency_host_host` = 1
+
- `mean_inoculum_host` = 1e1
+
- `mean_inoculum_vector` = 0
+
- `recovery_rate_host` = 1e-1
+
- `recovery_rate_vector` = 0
+
- `mortality_rate_host` = 0
+
- `mortality_rate_vector` = 0
+
- `recombine_in_host` = 1e-4
+
- `recombine_in_vector` = 0
+
- `num_crossover_host` = 1
+
- `num_crossover_vector` = 0
+
- `mutate_in_host` = 1e-6
+
- `mutate_in_vector` = 0
+
- `death_rate_host` = 0
+
- `death_rate_vector` = 0
+
- `birth_rate_host` = 0
+
- `birth_rate_vector` = 0
+
- `vertical_transmission_host` = 0
+
- `vertical_transmission_vector` = 0
+
- `inherit_protection_host` = 0
+
- `inherit_protection_vector` = 0
+
- `protection_upon_recovery_host` = None
+
- `protection_upon_recovery_vector` = None
+
+
**"vector-borne":**
+
+
- `num_loci` = 10
+
- `possible_alleles` = 'ATCG'
+
- `fitnessHost` = (lambda g: 1)
+
- `contactHost` = (lambda g: 1)
+
- `receiveContactHost` = (lambda g: 1)
+
- `mortalityHost` = (lambda g: 1)
+
- `natalityHost` = (lambda g: 1)
+
- `recoveryHost` = (lambda g: 1)
+
- `migrationHost` = (lambda g: 1)
+
- `populationContactHost` = (lambda g: 1)
+
- `receivePopulationContactHost` = (lambda g: 1)
+
- `mutationHost` = (lambda g: 1)
+
- `recombinationHost` = (lambda g: 1)
+
- `fitnessVector` = (lambda g: 1)
+
- `contactVector` = (lambda g: 1)
+
- `receiveContactVector` = (lambda g: 1)
+
- `mortalityVector` = (lambda g: 1)
+
- `natalityVector` = (lambda g: 1)
+
- `recoveryVector` = (lambda g: 1)
+
- `migrationVector` = (lambda g: 1)
+
- `populationContactVector` = (lambda g: 1)
+
- `receivePopulationContactVector` = (lambda g: 1)
+
- `mutationVector` = (lambda g: 1)
+
- `recombinationVector` = (lambda g: 1)
+
- `contact_rate_host_vector` = 2e-1
+
- `transmission_efficiency_host_vector` = 1
+
- `transmission_efficiency_vector_host` = 1
+
- `contact_rate_host_host` = 0
+
- `transmission_efficiency_host_host` = 0
+
- `mean_inoculum_host` = 1e2
+
- `mean_inoculum_vector` = 1e0
+
- `recovery_rate_host` = 1e-1
+
- `recovery_rate_vector` = 1e-1
+
- `mortality_rate_host` = 0
+
- `mortality_rate_vector` = 0
+
- `recombine_in_host` = 0
+
- `recombine_in_vector` = 1e-4
+
- `num_crossover_host` = 0
+
- `num_crossover_vector` = 1
+
- `mutate_in_host` = 1e-6
+
- `mutate_in_vector` = 0
+
- `death_rate_host` = 0
+
- `death_rate_vector` = 0
+
- `birth_rate_host` = 0
+
- `birth_rate_vector` = 0
+
- `vertical_transmission_host` = 0
+
- `vertical_transmission_vector` = 0
+
- `inherit_protection_host` = 0
+
- `inherit_protection_vector` = 0
+
- `protection_upon_recovery_host` = None
+
- `protection_upon_recovery_vector` = None
+
+
Arguments:
+
name (String): name of setup to be used as a key in model setups dictionary.
+
+
Keyword arguments:
+
preset (None or String): preset setup to be used: "vector-borne" or "host-host", if
+
None, must define all other keyword arguments. Defaults to None.
+
num_loci (int>0): length of each pathogen genome string.
+
possible_alleles (String or list of Strings with num_loci elements): set of possible
+
characters in all genome string, or at each position in genome string.
+
fitnessHost (callable, takes a String argument and returns a number >= 0): function
+
that evaluates relative fitness in head-to-head competition for different genomes
+
within the same host.
+
contactHost (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying probability of a given host being chosen to be the
+
infector in a contact event, based on genome sequence of pathogen.
+
receiveContactHost (callable, takes a String argument and returns a number 0-1): function
+
that returns coefficient modifying probability of a given host being chosen to be
+
the infected in a contact event, based on genome sequence of pathogen.
+
mortalityHost (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying death rate for a given host, based on genome sequence
+
of pathogen.
+
natalityHost (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying birth rate for a given host, based on genome sequence
+
of pathogen.
+
recoveryHost (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying recovery rate for a given host based on genome sequence
+
of pathogen.
+
migrationHost (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying migration rate for a given host based on genome sequence
+
of pathogen.
+
populationContactHost (callable, takes a String argument and returns a number 0-1): function
+
that returns coefficient modifying population contact rate for a given host based on
+
genome sequence of pathogen.
+
mutationHost (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying mutation rate for a given host based on genome sequence
+
of pathogen.
+
recombinationHost (callable, takes a String argument and returns a number 0-1): function
+
that returns coefficient modifying recombination rate for a given host based on genome
+
sequence of pathogen.
+
fitnessVector (callable, takes a String argument and returns a number >=0): function that
+
evaluates relative fitness in head-to-head competition for different genomes within
+
the same vector.
+
contactVector (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying probability of a given vector being chosen to be the
+
infector in a contact event, based on genome sequence of pathogen.
+
receiveContactVector (callable, takes a String argument and returns a number 0-1): function
+
that returns coefficient modifying probability of a given vector being chosen to be the
+
infected in a contact event, based on genome sequence of pathogen.
+
mortalityVector (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying death rate for a given vector, based on genome sequence
+
of pathogen.
+
natalityVector (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying birth rate for a given vector, based on genome sequence
+
of pathogen.
+
recoveryVector (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying recovery rate for a given vector based on genome sequence
+
of pathogen.
+
migrationVector (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying migration rate for a given vector based on genome sequence
+
of pathogen.
+
populationContactVector (callable, takes a String argument and returns a number 0-1): function
+
that returns coefficient modifying population contact rate for a given vector based on
+
genome sequence of pathogen.
+
mutationVector (callable, takes a String argument and returns a number 0-1): function that
+
returns coefficient modifying mutation rate for a given vector based on genome sequence
+
of pathogen.
+
recombinationVector (callable, takes a String argument and returns a number 0-1): function
+
that returns coefficient modifying recombination rate for a given vector based on genome
+
sequence of pathogen.
+
contact_rate_host_vector (number >= 0): rate of host-vector contact events, not necessarily
+
transmission, assumes constant population density; evts/time.
+
transmission_efficiency_host_vector (float): fraction of host-vector contacts
+
that result in successful transmission.
+
transmission_efficiency_vector_host (float): fraction of vector-host contacts
+
that result in successful transmission.
+
contact_rate_host_host (number >= 0): rate of host-host contact events, not
+
necessarily transmission, assumes constant population density; evts/time.
+
transmission_efficiency_host_host (float): fraction of host-host contacts
+
that result in successful transmission.
+
mean_inoculum_host (int >= 0): mean number of pathogens that are transmitted from
+
a vector or host into a new host during a contact event.
+
mean_inoculum_vector (int >= 0) mean number of pathogens that are transmitted
+
from a host to a vector during a contact event.
+
recovery_rate_host (number >= 0): rate at which hosts clear all pathogens;
+
1/time.
+
recovery_rate_vector (number >= 0): rate at which vectors clear all pathogens
+
1/time.
+
recovery_rate_vector (number >= 0): rate at which vectors clear all pathogens
+
1/time.
+
mortality_rate_host (number 0-1): rate at which infected hosts die from disease.
+
mortality_rate_vector (number 0-1): rate at which infected vectors die from
+
disease.
+
recombine_in_host (number >= 0): rate at which recombination occurs in host;
+
evts/time.
+
recombine_in_vector (number >= 0): rate at which recombination occurs in vector;
+
evts/time.
+
num_crossover_host (number >= 0): mean of a Poisson distribution modeling the number
+
of crossover events of host recombination events.
+
num_crossover_vector (number >= 0): mean of a Poisson distribution modeling the
+
number of crossover events of vector recombination events.
+
mutate_in_host (number >= 0): rate at which mutation occurs in host; evts/time.
+
mutate_in_vector (number >= 0): rate at which mutation occurs in vector; evts/time.
+
death_rate_host (number >= 0): natural host death rate; 1/time.
+
death_rate_vector (number >= 0): natural vector death rate; 1/time.
+
birth_rate_host (number >= 0): infected host birth rate; 1/time.
+
birth_rate_vector (number >= 0): infected vector birth rate; 1/time.
+
vertical_transmission_host (number 0-1): probability that a host is infected by its
+
parent at birth.
+
vertical_transmission_vector (number 0-1): probability that a vector is infected by
+
its parent at birth.
+
inherit_protection_host (number 0-1): probability that a host inherits all
+
protection sequences from its parent.
+
inherit_protection_vector (number 0-1): probability that a vector inherits all
+
protection sequences from its parent.
+
protection_upon_recovery_host (None or array-like of length 2 with int 0-num_loci): defines
+
indexes in genome string that define substring to be added to host protection sequences
+
after recovery.
+
protection_upon_recovery_vector (None or array-like of length 2 with int 0-num_loci): defines
+
indexes in genome string that define substring to be added to vector protection sequences
+
after recovery.
+
"""
+
+
if preset == "vector-borne":
+
num_loci = 10 if num_loci is None else num_loci
+
possible_alleles = \
+
'ATCG' if possible_alleles is None else possible_alleles
+
fitnessHost = (lambda g: 1) if fitnessHost is None else fitnessHost
+
contactHost = (lambda g: 1) if contactHost is None else contactHost
+
receiveContactHost = \
+
(lambda g: 1) if receiveContactHost is None else receiveContactHost
+
mortalityHost = \
+
(lambda g: 1) if mortalityHost is None else mortalityHost
+
natalityHost = \
+
(lambda g: 1) if natalityHost is None else natalityHost
+
recoveryHost = \
+
(lambda g: 1) if recoveryHost is None else recoveryHost
+
migrationHost = \
+
(lambda g: 1) if migrationHost is None else migrationHost
+
populationContactHost = \
+
(lambda g: 1) if populationContactHost is None else populationContactHost
+
receivePopulationContactHost = \
+
(lambda g: 1) if receivePopulationContactHost is None else receivePopulationContactHost
+
mutationHost = \
+
(lambda g: 1) if mutationHost is None else mutationHost
+
recombinationHost = \
+
(lambda g: 1) if recombinationHost is None else recombinationHost
+
fitnessVector = \
+
(lambda g: 1) if fitnessVector is None else fitnessVector
+
contactVector = \
+
(lambda g: 1) if contactVector is None else contactVector
+
receiveContactVector = \
+
(lambda g: 1) if receiveContactVector is None else receiveContactVector
+
mortalityVector = \
+
(lambda g: 1) if mortalityVector is None else mortalityVector
+
natalityVector = \
+
(lambda g: 1) if natalityVector is None else natalityVector
+
recoveryVector = \
+
(lambda g: 1) if recoveryVector is None else recoveryVector
+
migrationVector = \
+
(lambda g: 1) if migrationVector is None else migrationVector
+
populationContactVector = \
+
(lambda g: 1) if populationContactVector is None else populationContactVector
+
receivePopulationContactVector = \
+
(lambda g: 1) if receivePopulationContactVector is None else receivePopulationContactVector
+
mutationVector = \
+
(lambda g: 1) if mutationVector is None else mutationVector
+
recombinationVector = \
+
(lambda g: 1) if recombinationVector is None else recombinationVector
+
contact_rate_host_vector = \
+
2e-1 if contact_rate_host_vector is None \
+
else contact_rate_host_vector
+
transmission_efficiency_host_vector = \
+
1 if transmission_efficiency_host_vector is None \
+
else transmission_efficiency_host_vector
+
transmission_efficiency_vector_host = \
+
1 if transmission_efficiency_vector_host is None \
+
else transmission_efficiency_vector_host
+
contact_rate_host_host = \
+
0 if contact_rate_host_host is None else contact_rate_host_host
+
transmission_efficiency_host_host = \
+
0 if transmission_efficiency_host_host is None \
+
else transmission_efficiency_host_host
+
mean_inoculum_host = \
+
1e2 if mean_inoculum_host is None else mean_inoculum_host
+
mean_inoculum_vector = \
+
1 if mean_inoculum_vector is None else mean_inoculum_vector
+
recovery_rate_host = \
+
1e-1 if recovery_rate_host is None else recovery_rate_host
+
recovery_rate_vector = \
+
1e-1 if recovery_rate_vector is None else recovery_rate_vector
+
mortality_rate_host = \
+
0 if mortality_rate_host is None else mortality_rate_host
+
mortality_rate_vector = \
+
0 if mortality_rate_vector is None else mortality_rate_vector
+
recombine_in_host = \
+
0 if recombine_in_host is None else recombine_in_host
+
recombine_in_vector = \
+
1e-4 if recombine_in_vector is None else recombine_in_vector
+
num_crossover_host = 0 \
+
if num_crossover_host is None else num_crossover_host
+
num_crossover_vector = \
+
1 if num_crossover_vector is None else num_crossover_vector
+
mutate_in_host = 1e-6 if mutate_in_host is None else mutate_in_host
+
mutate_in_vector = \
+
0 if mutate_in_vector is None else mutate_in_vector
+
death_rate_host = 0 if death_rate_host is None else death_rate_host
+
death_rate_vector = \
+
0 if death_rate_vector is None else death_rate_vector
+
birth_rate_host = 0 if birth_rate_host is None else birth_rate_host
+
birth_rate_vector = \
+
0 if birth_rate_vector is None else birth_rate_vector
+
vertical_transmission_host = \
+
0 if vertical_transmission_host is None \
+
else vertical_transmission_host
+
vertical_transmission_vector = \
+
0 if vertical_transmission_vector is None \
+
else vertical_transmission_vector
+
inherit_protection_host = \
+
0 if inherit_protection_host is None \
+
else inherit_protection_host
+
inherit_protection_vector = \
+
0 if inherit_protection_vector is None \
+
else inherit_protection_vector
+
protection_upon_recovery_host = protection_upon_recovery_host
+
protection_upon_recovery_vector = protection_upon_recovery_vector
+
+
elif preset == "host-host":
+
num_loci = 10 if num_loci is None else num_loci
+
possible_alleles = \
+
'ATCG' if possible_alleles is None else possible_alleles
+
fitnessHost = (lambda g: 1) if fitnessHost is None else fitnessHost
+
contactHost = (lambda g: 1) if contactHost is None else contactHost
+
receiveContactHost = \
+
(lambda g: 1) if receiveContactHost is None else receiveContactHost
+
mortalityHost = \
+
(lambda g: 1) if mortalityHost is None else mortalityHost
+
natalityHost = \
+
(lambda g: 1) if natalityHost is None else natalityHost
+
recoveryHost = \
+
(lambda g: 1) if recoveryHost is None else recoveryHost
+
migrationHost = \
+
(lambda g: 1) if migrationHost is None else migrationHost
+
populationContactHost = \
+
(lambda g: 1) if populationContactHost is None else populationContactHost
+
receivePopulationContactHost = \
+
(lambda g: 1) if receivePopulationContactHost is None else receivePopulationContactHost
+
mutationHost = \
+
(lambda g: 1) if mutationHost is None else mutationHost
+
recombinationHost = \
+
(lambda g: 1) if recombinationHost is None else recombinationHost
+
fitnessVector = \
+
(lambda g: 1) if fitnessVector is None else fitnessVector
+
contactVector = \
+
(lambda g: 1) if contactVector is None else contactVector
+
receiveContactVector = \
+
(lambda g: 1) if receiveContactVector is None else receiveContactVector
+
mortalityVector = \
+
(lambda g: 1) if mortalityVector is None else mortalityVector
+
natalityVector = \
+
(lambda g: 1) if natalityVector is None else natalityVector
+
recoveryVector = \
+
(lambda g: 1) if recoveryVector is None else recoveryVector
+
mortality_rate_host = \
+
0 if mortality_rate_host is None else mortality_rate_host
+
mortality_rate_vector = \
+
0 if mortality_rate_vector is None else mortality_rate_vector
+
migrationVector = \
+
(lambda g: 1) if migrationVector is None else migrationVector
+
populationContactVector = \
+
(lambda g: 1) if populationContactVector is None else populationContactVector
+
receivePopulationContactVector = \
+
(lambda g: 1) if receivePopulationContactVector is None else receivePopulationContactVector
+
mutationVector = \
+
(lambda g: 1) if mutationVector is None else mutationVector
+
recombinationVector = \
+
(lambda g: 1) if recombinationVector is None else recombinationVector
+
contact_rate_host_vector = \
+
0 if contact_rate_host_vector is None \
+
else contact_rate_host_vector
+
transmission_efficiency_host_vector = \
+
0 if transmission_efficiency_host_vector is None \
+
else transmission_efficiency_host_vector
+
transmission_efficiency_vector_host = \
+
0 if transmission_efficiency_vector_host is None \
+
else transmission_efficiency_vector_host
+
contact_rate_host_host = \
+
2e-1 if contact_rate_host_host is None \
+
else contact_rate_host_host
+
transmission_efficiency_host_host = \
+
1 if transmission_efficiency_host_host is None \
+
else transmission_efficiency_host_host
+
mean_inoculum_host = \
+
1e1 if mean_inoculum_host is None else mean_inoculum_host
+
mean_inoculum_vector = \
+
0 if mean_inoculum_vector is None else mean_inoculum_vector
+
recovery_rate_host = \
+
1e-1 if recovery_rate_host is None else recovery_rate_host
+
recovery_rate_vector = \
+
0 if recovery_rate_vector is None else recovery_rate_vector
+
recombine_in_host = \
+
1e-4 if recombine_in_host is None else recombine_in_host
+
recombine_in_vector = \
+
0 if recombine_in_vector is None else recombine_in_vector
+
num_crossover_host = 1 \
+
if num_crossover_host is None else num_crossover_host
+
num_crossover_vector = \
+
0 if num_crossover_vector is None else num_crossover_vector
+
mutate_in_host = \
+
1e-6 if mutate_in_host is None else mutate_in_host
+
mutate_in_vector = \
+
0 if mutate_in_vector is None else mutate_in_vector
+
death_rate_host = \
+
0 if death_rate_host is None else death_rate_host
+
death_rate_vector = \
+
0 if death_rate_vector is None else death_rate_vector
+
birth_rate_host = 0 if birth_rate_host is None else birth_rate_host
+
birth_rate_vector = \
+
0 if birth_rate_vector is None else birth_rate_vector
+
vertical_transmission_host = \
+
0 if vertical_transmission_host is None \
+
else vertical_transmission_host
+
vertical_transmission_vector = \
+
0 if vertical_transmission_vector is None \
+
else vertical_transmission_vector
+
inherit_protection_host = \
+
0 if inherit_protection_host is None \
+
else inherit_protection_host
+
inherit_protection_vector = \
+
0 if inherit_protection_vector is None \
+
else inherit_protection_vector
+
protection_upon_recovery_host = protection_upon_recovery_host
+
protection_upon_recovery_vector = protection_upon_recovery_vector
+
+
self.setups[name] = Setup(
+
name,
+
num_loci, possible_alleles,
+
fitnessHost, contactHost, receiveContactHost, mortalityHost,
+
natalityHost, recoveryHost, migrationHost,
+
populationContactHost, receivePopulationContactHost,
+
mutationHost, recombinationHost,
+
fitnessVector, contactVector, receiveContactVector, mortalityVector,
+
natalityVector,recoveryVector, migrationVector,
+
populationContactVector, receivePopulationContactVector,
+
mutationVector, recombinationVector,
+
contact_rate_host_vector,
+
transmission_efficiency_host_vector,
+
transmission_efficiency_vector_host,
+
contact_rate_host_host,
+
transmission_efficiency_host_host,
+
mean_inoculum_host, mean_inoculum_vector,
+
recovery_rate_host, recovery_rate_vector,
+
mortality_rate_host,mortality_rate_vector,
+
recombine_in_host, recombine_in_vector,
+
num_crossover_host, num_crossover_vector,
+
mutate_in_host, mutate_in_vector, death_rate_host,death_rate_vector,
+
birth_rate_host, birth_rate_vector,
+
vertical_transmission_host, vertical_transmission_vector,
+
inherit_protection_host, inherit_protection_vector,
+
protection_upon_recovery_host, protection_upon_recovery_vector
+
)
+
+
+
+
[docs]
+
def newIntervention(self, time, method_name, args):
+
"""Create a new intervention to be carried out at a specific time.
+
+
Arguments:
+
time (number >= 0): time at which intervention will take place.
+
method_name (String): intervention to be carried out, must correspond to the
+
name of a method of the `Model` object.
+
args (array-like): contains arguments for function in positinal order.
+
"""
+
+
self.interventions.append( Intervention(time, method_name, args, self) )
+
+
+
+
[docs]
+
def addCustomConditionTracker(self, condition_id, trackerFunction):
+
"""Add a function to track occurrences of custom events in simulation.
+
+
Adds function `trackerFunction` to dictionary `custom_condition_trackers`
+
under key `condition_id`. Function `trackerFunction` will be executed at
+
every event in the simulation. Every time True is returned,
+
the simulation time will be stored under the corresponding `condition_id`
+
key inside `global_trackers['custom_condition']`.
+
+
Arguments:
+
condition_id (String): ID of this specific condition-
+
trackerFunction (callable): function that take a `Model` object as argument
+
and returns True or False.
+
"""
+
+
self.custom_condition_trackers['condition_id'] = trackerFunction
+
self.global_trackers['custom_conditions']['condition_id'] = []
+
+
+
+
[docs]
+
def run(self,t0,tf,time_sampling=0,host_sampling=0,vector_sampling=0):
+
"""Simulate model for a specified time between two time points.
+
+
Simulates a time series using the Gillespie algorithm.
+
+
Saves a dictionary containing model state history, with `keys=times` and
+
`values=Model` objects with model snapshot at that time point under this
+
model's history attribute.
+
+
Arguments:
+
t0 (number >= 0): initial time point to start simulation at.
+
tf (number >= 0): initial time point to end simulation at.
+
+
Keyword arguments:
+
time_sampling (int): how many events to skip before saving a snapshot of the
+
system state (saves all by default), if <0, saves only final state. Defaults to 0.
+
host_sampling (int >= 0): how many hosts to skip before saving one in a snapshot
+
of the system state (saves all by default). Defaults to 0.
+
vector_sampling (int >= 0): how many vectors to skip before saving one in a
+
snapshot of the system state (saves all by default). Defaults to 0.
+
"""
+
+
sim = Gillespie(self)
+
self.history = sim.run(
+
t0, tf, time_sampling, host_sampling, vector_sampling
+
)
+
+
+
+
[docs]
+
def runReplicates(
+
self,t0,tf,replicates,host_sampling=0,vector_sampling=0,n_cores=0,
+
**kwargs):
+
"""Simulate replicates of a model, save only end results.
+
+
Simulates replicates of a time series using the Gillespie algorithm.
+
+
Saves a dictionary containing model end state state, with `keys=times` and
+
`values=Model` objects with model snapshot. The time is the final timepoint.
+
+
Arguments:
+
t0 (number >= 0): initial time point to start simulation at.
+
tf (number >= 0): initial time point to end simulation at.
+
replicates (int >= 1): how many replicates to simulate.
+
+
Keyword arguments:
+
host_sampling (int >= 0): how many hosts to skip before saving one in a snapshot
+
of the system state (saves all by default). Defaults to 0.
+
vector_sampling (int >= 0): how many vectors to skip before saving one in a
+
snapshot of the system state (saves all by default). Defaults to 0.
+
n_cores (int >= 0): number of cores to parallelize file export across, if 0, all
+
cores available are used. Defaults to 0.
+
**kwargs: additional arguents for joblib multiprocessing.
+
+
Returns:
+
List of `Model` objects with the final snapshots.
+
"""
+
+
if not n_cores:
+
n_cores = jl.cpu_count()
+
+
print('Starting parallel simulations...')
+
+
def run(sim_num):
+
model = self.deepCopy()
+
sim = Gillespie(model)
+
mod = sim.run(
+
t0,tf,time_sampling=-1,
+
host_sampling=host_sampling,vector_sampling=vector_sampling
+
)[tf]
+
mod.history = { tf:mod }
+
return mod
+
+
return jl.Parallel(n_jobs=n_cores, verbose=10, **kwargs) (
+
jl.delayed( run ) (_) for _ in range(replicates)
+
)
+
+
+
+
[docs]
+
def runParamSweep(
+
self,t0,tf,setup_id,
+
param_sweep_dic={},
+
host_population_size_sweep={}, vector_population_size_sweep={},
+
host_migration_sweep_dic={}, vector_migration_sweep_dic={},
+
host_host_population_contact_sweep_dic={},
+
host_vector_population_contact_sweep_dic={},
+
vector_host_population_contact_sweep_dic={},
+
replicates=1,host_sampling=0,vector_sampling=0,n_cores=0,
+
**kwargs):
+
"""Simulate a parameter sweep with a model, save only end results.
+
+
Simulates variations of a time series using the Gillespie algorithm.
+
+
Saves a dictionary containing model end state state, with `keys=times` and
+
`values=Model` objects with model snapshot. The time is the final
+
timepoint.
+
+
Arguments:
+
t0 (number >= 0): initial time point to start simulation at.
+
tf (number >= 0): initial time point to end simulation at.
+
setup_id (String): ID of setup to be assigned.
+
+
Keyword arguments:
+
param_sweep_dic -- dictionary with keys=parameter names (attributes of
+
Setup), values=list of values for parameter (list, class of elements
+
depends on parameter)
+
host_population_size_sweep -- dictionary with keys=population IDs
+
(Strings), values=list of values with host population sizes
+
(must be greater than original size set for each population, list of
+
numbers)
+
vector_population_size_sweep -- dictionary with keys=population IDs
+
(Strings), values=list of values with vector population sizes
+
(must be greater than original size set for each population, list of
+
numbers)
+
host_migration_sweep_dic -- dictionary with keys=population IDs of
+
origin and destination, separated by a colon ';' (Strings),
+
values=list of values (list of numbers)
+
vector_migration_sweep_dic -- dictionary with keys=population IDs of
+
origin and destination, separated by a colon ';' (Strings),
+
values=list of values (list of numbers)
+
host_host_population_contact_sweep_dic -- dictionary with
+
keys=population IDs of origin and destination, separated by a colon
+
';' (Strings), values=list of values (list of numbers)
+
host_vector_population_contact_sweep_dic -- dictionary with
+
keys=population IDs of origin and destination, separated by a colon
+
';' (Strings), values=list of values (list of numbers)
+
vector_host_population_contact_sweep_dic -- dictionary with
+
keys=population IDs of origin and destination, separated by a colon
+
';' (Strings), values=list of values (list of numbers)
+
replicates -- how many replicates to simulate (int >= 1)
+
host_sampling -- how many hosts to skip before saving one in a snapshot
+
of the system state (saves all by default) (int >= 0, default 0)
+
vector_sampling -- how many vectors to skip before saving one in a
+
snapshot of the system state (saves all by default)
+
(int >= 0, default 0)
+
n_cores -- number of cores to parallelize file export across, if 0, all
+
cores available are used (default 0; int >= 0)
+
**kwargs -- additional arguents for joblib multiprocessing
+
+
Returns:
+
DataFrame with parameter combinations, list of Model objects with the
+
final snapshots.
+
"""
+
+
if not n_cores:
+
n_cores = jl.cpu_count()
+
+
for p in host_population_size_sweep:
+
param_sweep_dic['pop_size_host:'+p] = host_population_size_sweep[p]
+
+
for p in vector_population_size_sweep:
+
param_sweep_dic['pop_size_vector:'+p] = vector_population_size_sweep[p]
+
+
for p in host_migration_sweep_dic:
+
param_sweep_dic['migrate_host:'+p] = host_migration_sweep_dic[p]
+
+
for p in vector_migration_sweep_dic:
+
param_sweep_dic['migrate_vector:'+p] = vector_migration_sweep_dic[p]
+
+
for p in host_host_population_contact_sweep_dic:
+
param_sweep_dic['population_contact_host_host:'+p] \
+
= host_host_population_contact_sweep_dic[p]
+
+
for p in host_vector_population_contact_sweep_dic:
+
param_sweep_dic['population_contact_host_vector:'+p] \
+
= host_vector_population_contact_sweep_dic[p]
+
+
for p in vector_host_population_contact_sweep_dic:
+
param_sweep_dic['population_contact_vector_host:'+p] \
+
= vector_host_population_contact_sweep_dic[p]
+
+
if len(param_sweep_dic) == 0:
+
raise ValueError(
+
'param_sweep_dic, host_migration_sweep_dic, vector_migration_sweep_dic, host_host_population_contact_sweep_dic, host_vector_population_contact_sweep_dic, and vector_host_population_contact_sweep_dic cannot all be empty in runParamSweep()'
+
)
+
+
params = param_sweep_dic.keys()
+
value_lists = [ param_sweep_dic[param] for param in params ]
+
combinations = list( it.product( *value_lists ) ) * replicates
+
+
param_df = pd.DataFrame(combinations)
+
param_df.columns = params
+
results = {}
+
+
print('Starting parallel simulations...')
+
+
def run(param_values):
+
model = self.deepCopy()
+
for i,param_name in enumerate(params):
+
if ':' in param_name:
+
pops = param_name.split(':')[1].split(';')
+
if 'pop_size_host:' in param_name:
+
pop = cp.deepcopy( model.populations[pops[0]] )
+
add_hosts = param_values[i] - len(pop.hosts)
+
if add_hosts < 0:
+
raise ValueError(
+
'Value ' + str(param_values[i]) + ' assigned to ' + pops[0] + ' in host_population_size_sweep must be greater or equal to the population\'s original number of hosts.'
+
)
+
else:
+
pop.addHosts(add_hosts)
+
model.populations[pops[0]] = pop
+
pop.model = model
+
+
elif 'pop_size_vector:' in param_name:
+
pop = cp.deepcopy( model.populations[pops[0]] )
+
add_vectors = param_values[i] - len(pop.vectors)
+
if add_vectors < 0:
+
raise ValueError(
+
'Values ' + str(param_values[i]) + ' assigned to ' + pops[0] + ' in vector_population_size_sweep must be greater or equal to the population\'s original number of vectors.'
+
)
+
else:
+
pop.addVectors(add_vectors)
+
model.populations[pops[0]] = pop
+
pop.model = model
+
+
elif 'migrate_host:' in param_name:
+
new_pops = [
+
cp.deepcopy( model.populations[pops[0]] ),
+
cp.deepcopy( model.populations[pops[1]] )
+
]
+
new_pops[0].model = model
+
new_pops[1].model = model
+
model.populations[pops[0]] = new_pops[0]
+
model.populations[pops[1]] = new_pops[1]
+
model.linkPopulationsHostMigration(
+
new_pops[0],new_pops[1],param_values[i]
+
)
+
elif 'migrate_vector:' in param_name:
+
new_pops = [
+
cp.deepcopy( model.populations[pops[0]] ),
+
cp.deepcopy( model.populations[pops[1]] )
+
]
+
new_pops[0].model = model
+
new_pops[1].model = model
+
model.populations[pops[0]] = new_pops[0]
+
model.populations[pops[1]] = new_pops[1]
+
model.linkPopulationsVectorMigration(
+
pops[0],pops[1],param_values[i]
+
)
+
elif 'population_contact_host_host:' in param_name:
+
new_pops = [
+
cp.deepcopy( model.populations[pops[0]] ),
+
cp.deepcopy( model.populations[pops[1]] )
+
]
+
new_pops[0].model = model
+
new_pops[1].model = model
+
model.populations[pops[0]] = new_pops[0]
+
model.populations[pops[1]] = new_pops[1]
+
model.linkPopulationsHostHostContact(
+
pops[0],pops[1],param_values[i]
+
)
+
model.linkPopulationsHostHostContact(
+
pops[1],pops[0],param_values[i]
+
)
+
elif 'population_contact_host_vector:' in param_name:
+
new_pops = [
+
cp.deepcopy( model.populations[pops[0]] ),
+
cp.deepcopy( model.populations[pops[1]] )
+
]
+
new_pops[0].model = model
+
new_pops[1].model = model
+
model.populations[pops[0]] = new_pops[0]
+
model.populations[pops[1]] = new_pops[1]
+
model.linkPopulationsHostVectorContact(
+
pops[0],pops[1],param_values[i]
+
)
+
model.linkPopulationsHostVectorContact(
+
pops[1],pops[0],param_values[i]
+
)
+
elif 'population_contact_vector_host:' in param_name:
+
new_pops = [
+
cp.deepcopy( model.populations[pops[0]] ),
+
cp.deepcopy( model.populations[pops[1]] )
+
]
+
new_pops[0].model = model
+
new_pops[1].model = model
+
model.populations[pops[0]] = new_pops[0]
+
model.populations[pops[1]] = new_pops[1]
+
model.linkPopulationsVectorHostContact(
+
pops[0],pops[1],param_values[i]
+
)
+
model.linkPopulationsVectorHostContact(
+
pops[1],pops[0],param_values[i]
+
)
+
else:
+
setattr(model.setups[setup_id],param_name,param_values[i])
+
+
for name,pop in model.populations.items():
+
pop.setSetup( model.setups[pop.setup.id] )
+
+
sim = Gillespie(model)
+
mod = sim.run(
+
t0,tf,time_sampling=-1,
+
host_sampling=host_sampling,vector_sampling=vector_sampling
+
)[tf]
+
mod.history = { tf:mod }
+
return mod
+
+
return (
+
param_df,
+
jl.Parallel(n_jobs=n_cores, verbose=10, **kwargs) (
+
jl.delayed( run ) (param_values)
+
for param_values in combinations
+
)
+
)
+
+
+
+
[docs]
+
def copyState(self,host_sampling=0,vector_sampling=0):
+
"""Returns a slimmed-down representation of the current model state.
+
+
Keyword arguments:
+
host_sampling (int >= 0): how many hosts to skip before saving one in a snapshot
+
of the system state (saves all by default). Defaults to 0.
+
vector_sampling (int >= 0): how many vectors to skip before saving one in a
+
snapshot of the system state (saves all by default). Defaults to 0.
+
+
Returns:
+
Model object with current population host and vector lists.
+
"""
+
+
copy = Model()
+
+
copy.populations = {
+
id: p.copyState(host_sampling,vector_sampling)
+
for id,p in self.populations.items()
+
}
+
+
return copy
+
+
+
+
[docs]
+
def deepCopy(self):
+
"""Returns a full copy of the current model with inner references.
+
+
Returns:
+
Copied Model object.
+
"""
+
+
model = cp.deepcopy(self)
+
for intervention in model.interventions:
+
intervention.model = model
+
for pop in model.populations:
+
model.populations[pop].model = model
+
for h in model.populations[pop].hosts:
+
h.population = model.populations[pop]
+
for v in model.populations[pop].vectors:
+
v.population = model.populations[pop]
+
+
return model
+
+
+
+
### Output and Plots: ###
+
+
+
[docs]
+
def saveToDataFrame(self,save_to_file,n_cores=0,**kwargs):
+
"""Save status of model to dataframe, write to file location given.
+
+
Creates a pandas Dataframe in long format with the given model history,
+
with one host or vector per simulation time in each row, and columns:
+
+
- Time - simulation time of entry
+
- Population - ID of this host/vector's population
+
- Organism - host/vector
+
- ID - ID of host/vector
+
- Pathogens - all genomes present in this host/vector separated by ';'
+
- Protection - all genomes present in this host/vector separated by ';'
+
- Alive - whether host/vector is alive at this time, True/False
+
+
Arguments:
+
save_to_file (String): file path and name to save model data under.
+
+
Keyword arguments:
+
n_cores (int >= 0): number of cores to parallelize file export across, if 0, all
+
cores available are used. Defaults to 0.
+
**kwargs: additional arguents for joblib multiprocessing.
+
+
Returns:
+
`pandas dataframe` with model history as described above.
+
"""
+
+
data = saveToDf(
+
self.history,save_to_file,n_cores,**kwargs
+
)
+
+
return data
+
+
+
+
[docs]
+
def getPathogens(self, dat, save_to_file=""):
+
"""Create Dataframe with counts for all pathogen genomes in data.
+
+
Returns sorted pandas Dataframe with counts for occurrences of all
+
pathogen genomes in data passed.
+
+
Arguments:
+
data (pandas DataFrame): dataframe with model history as produced by `saveToDf` function.
+
+
Keyword arguments:
+
save_to_file (String): file path and name to save model data under, no saving
+
occurs if empty string. Defaults to "".
+
+
Returns:
+
`pandas dataframe` with Series as described above.
+
"""
+
+
return getPathogens(dat, save_to_file=save_to_file)
+
+
+
+
[docs]
+
def getProtections(self, dat, save_to_file=""):
+
"""Create Dataframe with counts for all protection sequences in data.
+
+
Returns sorted `pandas Dataframe` with counts for occurrences of all
+
protection sequences in data passed.
+
+
Arguments:
+
data (pandas DataFrame): dataframe with model history as produced by `saveToDf` function.
+
+
Keyword arguments:
+
save_to_file (String): file path and name to save model data under, no saving
+
occurs if empty string. Defaults to "".
+
+
Returns:
+
pandas DataFrame with Series as described above.
+
"""
+
+
return getProtections(dat, save_to_file=save_to_file)
+
+
+
+
[docs]
+
def populationsPlot(
+
self, file_name, data, compartment='Infected',
+
hosts=True, vectors=False, num_top_populations=7,
+
track_specific_populations=[], save_data_to_file="",
+
x_label='Time', y_label='Hosts', figsize=(8, 4), dpi=200,
+
palette=CB_PALETTE, stacked=False):
+
"""Create plot with aggregated totals per population across time.
+
+
Creates a line or stacked line plot with dynamics of a compartment
+
across populations in the model, with one line for each population.
+
+
A host or vector is considered part of the recovered compartment
+
if it has protection sequences of any kind and is not infected.
+
+
Arguments:
+
file_name (String): file path, name, and extension to save plot under.
+
data (pandas DataFrame): dataframe with model history as produced by saveToDf function.
+
+
Keyword arguments:
+
compartment (String): subset of hosts/vectors to count totals of, can be either
+
'Naive','Infected','Recovered', or 'Dead'. Defaults to 'Infected'.
+
hosts (Boolean): whether to count hosts. Defaults to True.
+
vectors (Boolean) whether to count vectors. Defaults to False.
+
num_top_populations (int): how many populations to count separately and
+
include as columns, remainder will be counted under column "Other";
+
if <0, includes all populations in model. Defaults to 7.
+
track_specific_populations (list of Strings): contains IDs of specific populations to
+
have as a separate column if not part of the top num_top_populations
+
populations. Defaults to [].
+
save_data_to_file (String): file path and name to save model plot data under,
+
no saving occurs if empty string. Defaults to "".
+
x_label (String): X axis title. Defaults to 'Time'.
+
y_label (String): Y axis title. Defaults to 'Hosts'.
+
legend_title (String): legend title. Defaults to 'Population'.
+
legend_values (list of Strings): labels for each trace, if empty list, uses population
+
IDs. Defaults to [].
+
figsize (array-like of two ints): dimensions of figure. Defaults to (8,4).
+
dpi (int): figure resolution. Defaults to 200.
+
palette (list of color Strings): color palette to use for traces. Defaults to `CB_PALETTE`.
+
stacked (Boolean): whether to draw a regular line plot instead of a stacked one. Defaults to False.
+
+
Returns:
+
`axis object` for plot with model population dynamics as described above.
+
"""
+
+
return populationsPlot(
+
file_name, data, compartment=compartment, hosts=hosts,
+
vectors=vectors, num_top_populations=num_top_populations,
+
track_specific_populations=track_specific_populations,
+
save_data_to_file=save_data_to_file,
+
x_label=x_label, y_label=y_label, figsize=figsize, dpi=dpi,
+
palette=palette, stacked=stacked
+
)
+
+
+
+
[docs]
+
def compartmentPlot(
+
self, file_name, data, populations=[], hosts=True, vectors=False,
+
save_data_to_file="", x_label='Time', y_label='Hosts',
+
figsize=(8, 4), dpi=200, palette=CB_PALETTE, stacked=False):
+
"""Create plot with number of naive,inf,rec,dead hosts/vectors vs. time.
+
+
Creates a line or stacked line plot with dynamics of all compartments
+
(naive, infected, recovered, dead) across selected populations in the
+
model, with one line for each compartment.
+
+
A host or vector is considered part of the recovered compartment
+
if it has protection sequences of any kind and is not infected.
+
+
Arguments:
+
file_name (String): file path, name, and extension to save plot under.
+
data (pandas DataFrame): dataframe with model history as produced by `saveToDf` function.
+
+
Keyword arguments:
+
populations (list of Strings): IDs of populations to include in analysis; if empty, uses
+
all populations in model. Defaults to [].
+
hosts (Boolean): whether to count hosts. Defaults to True.
+
vectors (Boolean): whether to count vectors. Defaults to False.
+
save_data_to_file (String): file path and name to save model data under, no
+
saving occurs if empty string. Defaults to "".
+
x_label (String): X axis title. Defaults to 'Time'.
+
y_label (String): Y axis title. Defaults to 'Hosts'.
+
legend_title (String): legend title. Defaults to 'Population'.
+
legend_values (list of Strings): labels for each trace, if empty list, uses population
+
IDs. Defaults to [].
+
figsize (array-like of two ints): dimensions of figure. Defaults to (8,4).
+
dpi (int)): figure resolution. Defaults to 200.
+
palette (list of color Strings): color palette to use for traces. Defaults to `CB_PALETTE`.
+
stacked -- whether to draw a regular line plot instead of a stacked one
+
(default False, Boolean)
+
+
Returns:
+
axis object for plot with model compartment dynamics as described above
+
"""
+
+
return compartmentPlot(
+
file_name, data, populations=populations, hosts=hosts,
+
vectors=vectors, save_data_to_file=save_data_to_file,
+
x_label=x_label, y_label=y_label, figsize=figsize, dpi=dpi,
+
palette=palette, stacked=stacked
+
)
+
+
+
+
[docs]
+
def compositionPlot(
+
self, file_name, data, composition_dataframe=None, populations=[],
+
type_of_composition='Pathogens', hosts=True, vectors=False,
+
num_top_sequences=7, track_specific_sequences=[],
+
save_data_to_file="", x_label='Time', y_label='Infections',
+
figsize=(8, 4), dpi=200, palette=CB_PALETTE, stacked=True,
+
remove_legend=False, genomic_positions=[],population_fraction=False,
+
count_individuals_based_on_model=None,
+
legend_title='Genotype', legend_values=[], **kwargs):
+
"""Create plot with counts for pathogen genomes or resistance vs. time.
+
+
Creates a line or stacked line plot with dynamics of the pathogen
+
strains or protection sequences across selected populations in the
+
model, with one line for each pathogen genome or protection sequence
+
being shown.
+
+
Of note: sum of totals for all sequences in one time point does not
+
necessarily equal the number of infected hosts and/or vectors, given
+
multiple infections in the same host/vector are counted separately.
+
+
Arguments:
+
file_name (String): file path, name, and extension to save plot under.
+
data (pandas DataFrame): dataframe with model history as produced by `saveToDf` function.
+
+
Keyword arguments:
+
composition_dataframe (pandas DataFrame): output of compositionDf() if already computed
+
Defaults to None.
+
populations (list of Strings): IDs of populations to include in analysis; if empty, uses
+
all populations in model. Defaults to [].
+
type_of_composition (String) field of data to count totals of, can be either
+
'Pathogens' or 'Protection'. Defaults to 'Pathogens'.
+
hosts (Boolean) whether to count hosts. Defaults to True.
+
vectors (Boolean): whether to count vectors. Defaults to False.
+
num_top_sequences (int): how many sequences to count separately and include
+
as columns, remainder will be counted under column "Other"; if <0,
+
includes all genomes in model. Defaults to 7.
+
track_specific_sequences (list of Strings): contains specific sequences to have
+
as a separate column if not part of the top num_top_sequences
+
sequences. Defaults to [].
+
genomic_positions (list of lists of int): list in which each element is a list with loci
+
positions to extract (e.g. genomic_positions=[ [0,3], [5,6] ]
+
extracts positions 0, 1, 2, and 5 from each genome); if empty, takes
+
full genomes. Defaults to [].
+
count_individuals_based_on_model (None or Model): `Model` object with populations and
+
fitness functions used to evaluate the most fit pathogen genome in
+
each host/vector in order to count only a single pathogen per
+
host/vector, as opposed to all pathogens within each host/vector; if
+
None, counts all pathogens. Defaults to None.
+
save_data_to_file (String): file path and name to save model data under, no
+
saving occurs if empty string. Defaults to "".
+
x_label (String): X axis title. Defaults to 'Time'.
+
y_label (String): Y axis title. Defaults to 'Hosts'.
+
legend_title (String): legend title. Defaults to 'Population'.
+
legend_values (list of Strings): labels for each trace, if empty list, uses population
+
IDs. Defaults to [].
+
figsize (array-like of two ints): dimensions of figure. Defaults to (8,4).
+
dpi (int): figure resolution. Defaults to 200.
+
palette (list of color Strings): color palette to use for traces. Defaults to `CB_PALETTE`.
+
stacked (Boolean): whether to draw a regular line plot instead of a stacked one. Defaults to False.
+
remove_legend (Boolean): whether to print the sequences on the figure legend
+
instead of printing them on a separate csv file. Defaults to True.
+
**kwargs: additional arguents for joblib multiprocessing.
+
+
Returns:
+
axis object for plot with model sequence composition dynamics as described.
+
"""
+
+
return compositionPlot(
+
file_name, data,
+
composition_dataframe=composition_dataframe,populations=populations,
+
type_of_composition=type_of_composition, hosts=hosts,
+
vectors=vectors, num_top_sequences=num_top_sequences,
+
track_specific_sequences=track_specific_sequences,
+
save_data_to_file=save_data_to_file,
+
x_label=x_label, y_label=y_label, figsize=figsize, dpi=dpi,
+
palette=palette, stacked=stacked, remove_legend=remove_legend,
+
genomic_positions=genomic_positions,
+
count_individuals_based_on_model=count_individuals_based_on_model,
+
population_fraction=population_fraction, legend_title=legend_title,
+
legend_values=legend_values,
+
**kwargs
+
)
+
+
+
+
[docs]
+
def clustermap(
+
self,
+
file_name, data, num_top_sequences=-1, track_specific_sequences=[],
+
seq_names=[], n_cores=0, method='weighted', metric='euclidean',
+
save_data_to_file="", legend_title='Distance', legend_values=[],
+
figsize=(10,10), dpi=200, color_map=DEF_CMAP):
+
"""Create a heatmap and dendrogram for pathogen genomes in data passed.
+
+
Arguments:
+
file_name (String): file path, name, and extension to save plot under.
+
data (pandas DataFrame): dataframe with model history as produced by `saveToDf`
+
function.
+
+
Keyword arguments:
+
num_top_sequences (int): how many sequences to include in matrix; if <0,
+
includes all genomes in data passed. Defaults to -1.
+
track_specific_sequences (list of Strings): contains specific sequences to include
+
in matrix if not part of the top num_top_sequences sequences. Defaults to [].
+
seq_names (list ofStrings): list with names to be used for sequence labels in matrix
+
must be of same length as number of sequences to be displayed; if empty, uses sequences
+
themselves. Defaults to [].
+
n_cores (int >= 0): number of cores to parallelize distance compute across, if 0,
+
all cores available are used. Defaults to 0.
+
method (String): clustering algorithm to use with seaborn clustermap. Defaults to 'weighted'.
+
metric (String): distance metric to use with seaborn clustermap. Defaults to 'euclidean'.
+
save_data_to_file (String): file path and name to save model data under, no
+
saving occurs if empty string. Defaults to "".
+
legend_title (String): legend title. Defaults to 'Distance'.
+
figsize (array-like of two ints): dimensions of figure. Defaults to (8,4).
+
dpi (int): figure resolution. Defaults to 200.
+
color_map (matplotlib cmap object): color map to use for traces. Defaults to `DEF_CMAP`.
+
+
Returns:
+
figure object for plot with heatmap and dendrogram as described.
+
"""
+
+
return clustermap(
+
file_name, data, num_top_sequences=num_top_sequences,
+
track_specific_sequences=track_specific_sequences,
+
seq_names=seq_names, n_cores=n_cores, method=method,
+
metric=metric, save_data_to_file=save_data_to_file,
+
legend_title=legend_title, legend_values=legend_values,
+
figsize=figsize, dpi=dpi, color_map=color_map
+
)
+
+
+
+
[docs]
+
def pathogenDistanceHistory(
+
self,
+
data, samples=-1, num_top_sequences=-1, track_specific_sequences=[],
+
seq_names=[], n_cores=0, save_to_file=''):
+
"""Create DataFrame with pairwise Hamming distances for pathogen
+
sequences in data.
+
+
DataFrame has indexes and columns named according to genomes or argument
+
seq_names, if passed. Distance is measured as percent Hamming distance
+
from an optimal genome sequence.
+
+
Arguments:
+
data (pandas DataFrame): dataframe with model history as produced by `saveToDf`
+
function.
+
+
Keyword arguments:
+
samples (int): how many timepoints to uniformly sample from the total
+
timecourse; if <0, takes all timepoints. Defaults to 1.
+
num_top_sequences (int) how many sequences to include in matrix; if <0,
+
includes all genomes in data passed. Defaults to -1.
+
track_specific_sequences (list of Strings): contains specific sequences to include in
+
matrix if not part of the top num_top_sequences sequences. Defaults to [].
+
seq_names (list of Strings): list with names to be used for sequence labels in matrix
+
must be of same length as number of sequences to be displayed; if
+
empty, uses sequences themselves. Defaults to [].
+
n_cores (int >= 0): number of cores to parallelize distance compute across, if 0,
+
all cores available are used. Defaults to 0.
+
save_to_file (String): file path and name to save model data under, no saving
+
occurs if empty string. Defaults to "".
+
+
Returns:
+
pandas DataFrame with distance matrix as described above.
+
"""
+
return getPathogenDistanceHistoryDf(data,
+
samples=samples, num_top_sequences=num_top_sequences,
+
track_specific_sequences=track_specific_sequences,
+
seq_names=seq_names, n_cores=n_cores, save_to_file=save_to_file)
+
+
+
+
[docs]
+
def getGenomeTimes(
+
self,
+
data, samples=-1, num_top_sequences=-1, track_specific_sequences=[],
+
seq_names=[], n_cores=0, save_to_file=''):
+
"""Create DataFrame with times genomes first appeared during simulation.
+
+
Arguments:
+
data (pandas DataFrame): dataframe with model history as produced by `saveToDf`
+
function.
+
+
Keyword arguments:
+
samples (int): how many timepoints to uniformly sample from the total
+
timecourse; if <0, takes all timepoints. Defaults to 1.
+
save_to_file (String): file path and name to save model data under, no saving
+
occurs if empty string. Defaults to "".
+
n_cores (int): number of cores to parallelize across, if 0, all cores
+
available are used. Defaults to 0.
+
+
Returns:
+
pandas DataFrame with genomes and times as described above.
+
"""
+
return getGenomeTimesDf(data,
+
samples=samples, num_top_sequences=num_top_sequences,
+
track_specific_sequences=track_specific_sequences,
+
seq_names=seq_names, n_cores=n_cores, save_to_file=save_to_file)
+
+
+
+
+
[docs]
+
def getCompositionData(
+
self, data=None, populations=[], type_of_composition='Pathogens',
+
hosts=True, vectors=False, num_top_sequences=-1,
+
track_specific_sequences=[], genomic_positions=[],
+
count_individuals_based_on_model=None, save_data_to_file="",
+
n_cores=0, **kwargs):
+
"""Create dataframe with counts for pathogen genomes or resistance.
+
+
Creates a pandas Dataframe with dynamics of the pathogen strains or
+
protection sequences across selected populations in the model,
+
with one time point in each row and columns for pathogen genomes or
+
protection sequences.
+
+
Of note: sum of totals for all sequences in one time point does not
+
necessarily equal the number of infected hosts and/or vectors, given
+
multiple infections in the same host/vector are counted separately.
+
+
Keyword arguments:
+
data (pandas DataFrame): dataframe with model history as produced by `saveToDf`
+
function; if None, computes this dataframe and saves it under 'raw_data_'+'save_data_to_file'.
+
Defaults to None.
+
populations (list of Strings): IDs of populations to include in analysis; if empty, uses
+
all populations in model. Defaults to [].
+
type_of_composition (String): field of data to count totals of, can be either
+
'Pathogens' or 'Protection'. Defaults to 'Pathogens'.
+
hosts (Boolean): whether to count hosts. Defaults to True.
+
vectors (Boolean): whether to count vectors. Defaults to False.
+
num_top_sequences (int): how many sequences to count separately and include
+
as columns, remainder will be counted under column "Other"; if <0,
+
includes all genomes in model. Defaults to -1.
+
track_specific_sequences (list of Strings): contains specific sequences to have
+
as a separate column if not part of the top num_top_sequences
+
sequences. Defaults to [].
+
genomic_positions (list of lists of int): list in which each element is a list with
+
loci positions to extract (e.g. genomic_positions=[ [0,3], [5,6] ]
+
extracts positions 0, 1, 2, and 5 from each genome); if empty, takes
+
full genomes. Defaults to [].
+
count_individuals_based_on_model (None or Model object): Model object with populations and
+
fitness functions used to evaluate the most fit pathogen genome in
+
each host/vector in order to count only a single pathogen per
+
host/vector, asopposed to all pathogens within each host/vector; if
+
None, counts all pathogens. Defaults to None.
+
save_data_to_file (String): file path and name to save model data under, no
+
saving occurs if empty string. Defaults to "".
+
n_cores (int): number of cores to parallelize processing across, if 0, all
+
cores available are used. Defaults to 0.
+
**kwargs: additional arguents for joblib multiprocessing.
+
+
Returns:
+
pandas DataFrame with model sequence composition dynamics as described
+
above.
+
"""
+
+
if data is None:
+
data = saveToDf(
+
self.history,'raw_data_'+save_to_file,n_cores,verbose=verbose,
+
**kwargs
+
)
+
+
return compositionDf(
+
data, populations=populations,
+
type_of_composition=type_of_composition,
+
hosts=hosts, vectors=vectors, num_top_sequences=num_top_sequences,
+
track_specific_sequences=track_specific_sequences,
+
genomic_positions=genomic_positions,
+
count_individuals_based_on_model=count_individuals_based_on_model,
+
save_to_file=save_data_to_file, n_cores=n_cores, **kwargs
+
)
+
+
+
### Model interventions: ###
+
+
+
[docs]
+
def newPopulation(self, id, setup_name, num_hosts=0, num_vectors=0):
+
"""Create a new Population object with setup parameters.
+
+
If population ID is already in use, appends _2 to it
+
+
Arguments:
+
id (String): unique identifier for this population in the model.
+
setup_name (Setup object): setup object with parameters for this population.
+
+
Keyword arguments:
+
num_hosts (int >= 0): number of hosts to initialize population with. Defaults to 100.
+
num_vectors (int >= 0): number of vectors to initialize population with. Defaults to 100.
+
"""
+
+
if id in self.populations.keys():
+
id = id+'_2'
+
+
self.populations[id] = Population(
+
self, id, self.setups[setup_name], num_hosts, num_vectors
+
)
+
+
for p in self.populations:
+
self.populations[id].setHostMigrationNeighbor(self.populations[p],0)
+
self.populations[id].setVectorMigrationNeighbor(
+
self.populations[p],0
+
)
+
self.populations[id].setHostHostPopulationContactNeighbor(
+
self.populations[p],0
+
)
+
self.populations[id].setVectorHostPopulationContactNeighbor(
+
self.populations[p],0
+
)
+
self.populations[id].setHostVectorPopulationContactNeighbor(
+
self.populations[p],0
+
)
+
+
self.populations[p].setHostMigrationNeighbor(
+
self.populations[id],0
+
)
+
self.populations[p].setVectorMigrationNeighbor(
+
self.populations[id],0
+
)
+
self.populations[p].setHostHostPopulationContactNeighbor(
+
self.populations[id],0
+
)
+
self.populations[p].setVectorHostPopulationContactNeighbor(
+
self.populations[id],0
+
)
+
self.populations[p].setHostVectorPopulationContactNeighbor(
+
self.populations[id],0
+
)
+
+
+
+
[docs]
+
def linkPopulationsHostMigration(self, pop1_id, pop2_id, rate):
+
"""Set host migration rate from one population towards another.
+
+
Arguments:
+
pop1_id (String): origin population for which migration rate will be specified.
+
pop1_id (String): destination population for which migration rate will be
+
specified.
+
rate (number >= 0): migration rate from one population to the neighbor; evts/time.
+
"""
+
+
self.populations[pop1_id].setHostMigrationNeighbor(
+
self.populations[pop2_id], rate
+
)
+
+
+
+
[docs]
+
def linkPopulationsVectorMigration(self, pop1_id, pop2_id, rate):
+
"""Set vector migration rate from one population towards another.
+
+
Arguments:
+
pop1_id (String): origin population for which migration rate will be specified.
+
pop1_id (String): destination population for which migration rate will be
+
specified.
+
rate (number >= 0): migration rate from one population to the neighbor; evts/time.
+
"""
+
+
self.populations[pop1_id].setVectorMigrationNeighbor(
+
self.populations[pop2_id], rate
+
)
+
+
+
+
+
+
+
+
+
+
+
+
+
[docs]
+
def createInterconnectedPopulations(
+
self, num_populations, id_prefix, setup_name,
+
host_migration_rate=0, vector_migration_rate=0,
+
host_host_contact_rate=0,
+
host_vector_contact_rate=0, vector_host_contact_rate=0,
+
num_hosts=100, num_vectors=100):
+
"""Create new populations, link all of them to each other.
+
+
All populations in this cluster are linked with the same migration rate,
+
starting number of hosts and vectors, and setup parameters. Their IDs
+
are numbered onto prefix given as 'id_prefix_0', 'id_prefix_1',
+
'id_prefix_2', etc.
+
+
Arguments:
+
num_populations (int): number of populations to be created.
+
id_prefix (String): prefix for IDs to be used for this population in the model.
+
setup_name (Setup object): setup object with parameters for all populations.
+
+
Keyword arguments:
+
host_migration_rate (number >= 0): host migration rate between populations;
+
evts/time. Defaults to 0.
+
vector_migration_rate (number >= 0): vector migration rate between populations;
+
evts/time. Defaults to 0.
+
host_host_contact_rate (number >= 0): host-host inter-population contact rate
+
between populations; evts/time. Defaults to 0.
+
host_vector_contact_rate (number >= 0): host-vector inter-population contact rate
+
between populations; evts/time. Defaults to 0.
+
vector_host_contact_rate (number >= 0): vector-host inter-population contact rate
+
between populations; evts/time. Defaults to 0.
+
num_hosts (int): number of hosts to initialize population with. Defaults to 100.
+
num_vectors (int): number of hosts to initialize population with. Defaults to 100.
+
"""
+
+
new_pops = [
+
Population(
+
self, str(id_prefix) + str(i), self.setups[setup_name],
+
num_hosts, num_vectors
+
) for i in range(num_populations)
+
]
+
new_pop_ids = []
+
for pop in new_pops:
+
if pop.id in self.populations.keys():
+
pop.id = pop.id+'_2'
+
+
self.populations[pop.id] = pop
+
new_pop_ids.append(pop.id)
+
+
for p in self.populations:
+
pop.setHostMigrationNeighbor(self.populations[p],0)
+
pop.setVectorMigrationNeighbor(self.populations[p],0)
+
pop.setHostHostPopulationContactNeighbor(self.populations[p],0)
+
pop.setHostVectorPopulationContactNeighbor(self.populations[p],0)
+
pop.setVectorHostPopulationContactNeighbor(self.populations[p],0)
+
+
self.populations[p].setHostMigrationNeighbor(pop,0)
+
self.populations[p].setVectorMigrationNeighbor(pop,0)
+
self.populations[p].setHostHostPopulationContactNeighbor(pop,0)
+
self.populations[p].setHostVectorPopulationContactNeighbor(pop,0)
+
self.populations[p].setVectorHostPopulationContactNeighbor(pop,0)
+
+
for p1_id in new_pop_ids:
+
for p2_id in new_pop_ids:
+
self.linkPopulationsHostMigration(
+
p1_id,p2_id,host_migration_rate
+
)
+
self.linkPopulationsVectorMigration(
+
p1_id,p2_id,vector_migration_rate
+
)
+
self.linkPopulationsHostHostContact(
+
p1_id,p2_id,host_host_contact_rate
+
)
+
self.linkPopulationsHostVectorContact(
+
p1_id,p2_id,host_vector_contact_rate
+
)
+
self.linkPopulationsVectorHostContact(
+
p1_id,p2_id,vector_host_contact_rate
+
)
+
+
+
+
[docs]
+
def newHostGroup(self, pop_id, group_id, hosts=-1, type='any'):
+
"""Return a list of random hosts in population.
+
+
Arguments:
+
pop_id (String): ID of population to be sampled from.
+
group_id (String): ID to name group with.
+
+
Keyword arguments:
+
hosts (number): number of hosts to be sampled randomly: if <0, samples from
+
whole population; if <1, takes that fraction of population; if >=1,
+
samples that integer number of hosts. Defaults to -1.
+
type (String = {'healthy', 'infected', 'any'}): whether to sample healthy hosts only,
+
infected hosts only, or any hosts. Defaults to 'any'.
+
+
Returns:
+
list containing sampled hosts.
+
"""
+
+
self.groups[group_id] = self.populations[pop_id].newHostGroup(
+
hosts, type
+
)
+
+
+
+
[docs]
+
def newVectorGroup(self, pop_id, group_id, vectors=-1, type='any'):
+
"""Return a list of random vectors in population.
+
+
Arguments:
+
pop_id (String): ID of population to be sampled from.
+
group_id (String): ID to name group with.
+
+
Keyword arguments:
+
vectors (number): number of vectors to be sampled randomly: if <0, samples from
+
whole population; if <1, takes that fraction of population; if >=1,
+
samples that integer number of vectors. Defaults to -1.
+
type (String = {'healthy', 'infected', 'any'}): whether to sample healthy vectors only, infected vectors
+
only, or any vectors. Defaults to 'any'.
+
+
Returns:
+
list containing sampled vectors.
+
"""
+
+
self.groups[group_id] = self.populations[pop_id].newVectorGroup(
+
vectors, type
+
)
+
+
+
+
[docs]
+
def addHosts(self, pop_id, num_hosts):
+
"""Add a number of healthy hosts to population, return list with them.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
num_hosts (int): number of hosts to be added.
+
+
Returns:
+
list containing new hosts.
+
"""
+
+
self.populations[pop_id].addHosts(num_hosts)
+
+
+
+
[docs]
+
def addVectors(self, pop_id, num_vectors):
+
"""Add a number of healthy vectors to population, return list with them.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
num_vectors (int): number of vectors to be added.
+
+
Returns:
+
list containing new vectors.
+
"""
+
+
self.populations[pop_id].addVectors(num_vectors)
+
+
+
+
[docs]
+
def removeHosts(self, pop_id, num_hosts_or_list):
+
"""Remove a number of specified or random hosts from population.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
num_hosts_or_list (int or list of Hosts): number of hosts to be sampled randomly for removal
+
or list of hosts to be removed, must be hosts in this population.
+
"""
+
+
self.populations[pop_id].removeHosts(num_hosts_or_list)
+
+
+
+
[docs]
+
def removeVectors(self, pop_id, num_vectors_or_list):
+
"""Remove a number of specified or random vectors from population.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
num_vectors_or_list (int or list of Vectors): number of vectors to be sampled randomly for
+
removal or list of vectors to be removed, must be vectors in this
+
population.
+
"""
+
+
self.populations[pop_id].removeVectors(num_vectors_or_list)
+
+
+
+
[docs]
+
def addPathogensToHosts(self, pop_id, genomes_numbers, group_id=""):
+
"""Add specified pathogens to random hosts, optionally from a list.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
genomes_numbers (dict with keys=Strings, values=int) dictionary containing pathogen
+
genomes to add as keys and number of hosts each one will be added to as values.
+
+
Keyword arguments:
+
group_id (String): ID of specific hosts to sample from, if empty, samples
+
from whole population. Defaults to "".
+
"""
+
+
if group_id == "":
+
hosts = self.populations[pop_id].hosts
+
else:
+
hosts = self.groups[group_id]
+
+
self.populations[pop_id].addPathogensToHosts(genomes_numbers,hosts)
+
+
+
+
[docs]
+
def addPathogensToVectors(self, pop_id, genomes_numbers, group_id=""):
+
"""Add specified pathogens to random vectors, optionally from a list.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
genomes_numbers (dict with keys=Strings, values=int): dictionary containing pathogen
+
genomes to add as keys and number of vectors each one will be added to as values.
+
+
Keyword arguments:
+
group_id (String): ID of specific vectors to sample from, if empty, samples
+
from whole population. Defaults to "".
+
"""
+
+
if group_id == "":
+
vectors = self.populations[pop_id].vectors
+
else:
+
vectors = self.groups[group_id]
+
+
self.populations[pop_id].addPathogensToVectors(genomes_numbers,vectors)
+
+
+
+
[docs]
+
def treatHosts(self, pop_id, frac_hosts, resistance_seqs, group_id=""):
+
"""Treat random fraction of infected hosts against some infection.
+
+
Removes all infections with genotypes susceptible to given treatment.
+
Pathogens are removed if they are missing at least one of the sequences
+
in resistance_seqs from their genome. Removes this organism from
+
population infected list and adds to healthy list if appropriate.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
frac_hosts (number between 0 and 1): fraction of hosts considered to be randomly selected.
+
resistance_seqs (list of Strings): contains sequences required for treatment resistance.
+
+
Keyword arguments:
+
group_id (String): ID of specific hosts to sample from, if empty, samples
+
from whole population. Defaults to "".
+
"""
+
+
if group_id == "":
+
hosts = self.populations[pop_id].hosts
+
else:
+
hosts = self.groups[group_id]
+
+
self.populations[pop_id].treatHosts(frac_hosts,resistance_seqs,hosts)
+
+
+
+
[docs]
+
def treatVectors(self, pop_id, frac_vectors, resistance_seqs, group_id=""):
+
"""Treat random fraction of infected vectors agains some infection.
+
+
Removes all infections with genotypes susceptible to given treatment.
+
Pathogens are removed if they are missing at least one of the sequences
+
in resistance_seqs from their genome. Removes this organism from
+
population infected list and adds to healthy list if appropriate.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
frac_vectors (number between 0 and 1): fraction of vectors considered to be
+
randomly selected.
+
resistance_seqs (list of Strings): contains sequences required for treatment resistance.
+
+
Keyword arguments:
+
group_id (String): ID of specific vectors to sample from, if empty, samples
+
from whole population. Defaults to "".
+
"""
+
+
if group_id == "":
+
vectors = self.populations[pop_id].vectors
+
else:
+
vectors = self.groups[group_id]
+
+
self.populations[pop_id].treatVectors(
+
frac_vectors,resistance_seqs,vectors
+
)
+
+
+
+
[docs]
+
def protectHosts(
+
self, pop_id, frac_hosts, protection_sequence, group_id=""):
+
"""Protect a random fraction of infected hosts against some infection.
+
+
Adds protection sequence specified to a random fraction of the hosts
+
specified. Does not cure them if they are already infected.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
frac_hosts (number between 0 and 1): fraction of hosts considered to be
+
randomly selected.
+
protection_sequence (String): sequence against which to protect.
+
+
Keyword arguments:
+
group_id (String): ID of specific hosts to sample from, if empty, samples
+
from whole population. Defaults to "".
+
"""
+
+
if group_id == "":
+
hosts = self.populations[pop_id].hosts
+
else:
+
hosts = self.groups[group_id]
+
+
self.populations[pop_id].protectHosts(
+
frac_hosts,protection_sequence,hosts
+
)
+
+
+
+
[docs]
+
def protectVectors(
+
self, pop_id, frac_vectors, protection_sequence, group_id=""):
+
"""Protect a random fraction of infected vectors against some infection.
+
+
Adds protection sequence specified to a random fraction of the vectors
+
specified. Does not cure them if they are already infected.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
frac_vectors (number between 0 and 1): fraction of vectors considered to be randomly selected.
+
protection_sequence (String): sequence against which to protect.
+
+
Keyword arguments:
+
group_id (String): ID of specific vectors to sample from, if empty, samples
+
from whole population. Defaults to "".
+
"""
+
+
if group_id == "":
+
vectors = self.populations[pop_id].vectors
+
else:
+
vectors = self.groups[group_id]
+
+
self.populations[pop_id].protectVectors(
+
frac_vectors,protection_sequence,vectors
+
)
+
+
+
+
[docs]
+
def wipeProtectionHosts(self, pop_id, group_id=""):
+
"""Removes all protection sequences from hosts.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
+
Keyword arguments:
+
group_id (String): ID of specific hosts to sample from, if empty, samples from
+
whole from whole population. Defaults to "".
+
"""
+
+
if group_id == "":
+
hosts = self.populations[pop_id].hosts
+
else:
+
hosts = self.groups[group_id]
+
+
self.populations[pop_id].wipeProtectionHosts(hosts)
+
+
+
+
[docs]
+
def wipeProtectionVectors(self, pop_id, group_id=""):
+
"""Removes all protection sequences from vectors.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
+
Keyword arguments:
+
group_id (String): ID of specific vectors to sample from, if empty, samples
+
from whole population. Defaults to "".
+
"""
+
+
if group_id == "":
+
vectors = self.populations[pop_id].vectors
+
else:
+
vectors = self.groups[group_id]
+
+
self.populations[pop_id].wipeProtectionVectors(vectors)
+
+
+
### Modify population parameters: ###
+
+
+
[docs]
+
def setSetup(self, pop_id, setup_id):
+
"""Assign parameters stored in Setup object to this population.
+
+
Arguments:
+
pop_id (String): ID of population to be modified.
+
setup_id (String): ID of setup to be assigned.
+
"""
+
+
self.populations[pop_id].setSetup( self.setups[setup_id] )
+
+
+
### Utility: ###
+
+
+
[docs]
+
def customModelFunction(self, function):
+
"""Returns output of given function, passing this model as a parameter.
+
+
Arguments:
+
function (callable): function to be evaluated; must take a Model object as the
+
only parameter.
+
+
Returns:
+
output of function passed as parameter.
+
"""
+
+
return function(self)
+
+
+
### Preset fitness functions: ###
+
+
+
[docs]
+
@staticmethod
+
def peakLandscape(genome, peak_genome, min_value):
+
"""Return genome phenotype by decreasing with distance from optimal seq.
+
+
A purifying selection fitness function based on exponential decay of
+
fitness as genomes move away from the optimal sequence. Distance is
+
measured as percent Hamming distance from an optimal genome sequence.
+
+
Arguments:
+
genome (String): the genome to be evaluated.
+
peak_genome (String): the genome sequence to measure distance against, has
+
value of 1.
+
min_value (number 0-1): minimum value at maximum distance from optimal
+
genome.
+
+
Return:
+
value of genome (number).
+
"""
+
+
distance = td.hamming(genome, peak_genome) / len(genome)
+
value = np.exp( np.log( min_value ) * distance )
+
+
return value
+
+
+
+
[docs]
+
@staticmethod
+
def valleyLandscape(genome, valley_genome, min_value):
+
"""Return genome phenotype by increasing with distance from worst seq.
+
+
A disruptive selection fitness function based on exponential decay of
+
fitness as genomes move closer to the worst possible sequence. Distance
+
is measured as percent Hamming distance from the worst possible genome
+
sequence.
+
+
Arguments:
+
genome (String): the genome to be evaluated.
+
valley_genome (String): the genome sequence to measure distance against, has
+
value of min_value.
+
min_value (number 0-1): fitness value of worst possible genome.
+
+
Return:
+
value of genome (number).
+
"""
+
+
distance = td.hamming(genome, valley_genome) / len(genome)
+
value = np.exp( np.log( min_value ) * ( 1 - distance ) )
+
+
return value
+
+
+
+
+
+
+
+
+
+
+
+
+